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Design and evaluation of a mobile application to assist the self-monitoring of the chronic kidney disease in developing countries

Overview of attention for article published in BMC Medical Informatics and Decision Making, January 2018
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Title
Design and evaluation of a mobile application to assist the self-monitoring of the chronic kidney disease in developing countries
Published in
BMC Medical Informatics and Decision Making, January 2018
DOI 10.1186/s12911-018-0587-9
Pubmed ID
Authors

Alvaro Sobrinho, Leandro Dias da Silva, Angelo Perkusich, Maria Eliete Pinheiro, Paulo Cunha

Abstract

The chronic kidney disease (CKD) is a worldwide critical problem, especially in developing countries. CKD patients usually begin their treatment in advanced stages, which requires dialysis and kidney transplantation, and consequently, affects mortality rates. This issue is faced by a mobile health (mHealth) application (app) that aims to assist the early diagnosis and self-monitoring of the disease progression. A user-centered design (UCD) approach involving health professionals (nurse and nephrologists) and target users guided the development process of the app between 2012 and 2016. In-depth interviews and prototyping were conducted along with healthcare professionals throughout the requirements elicitation process. Elicited requirements were translated into a native mHealth app targeting the Android platform. Afterward, the Cohen's Kappa coefficient statistics was applied to evaluate the agreement between the app and three nephrologists who analyzed test results collected from 60 medical records. Finally, eight users tested the app and were interviewed about usability and user perceptions. A mHealth app was designed to assist the CKD early diagnosis and self-monitoring considering quality attributes such as safety, effectiveness, and usability. A global Kappa value of 0.7119 showed a substantial degree of agreement between the app and three nephrologists. Results of face-to-face interviews with target users indicated a good user satisfaction. However, the task of CKD self-monitoring proved difficult because most of the users did not fully understand the meaning of specific biomarkers (e.g., creatinine). The UCD approach provided mechanisms to develop the app based on the real needs of users. Even with no perfect Kappa degree of agreement, results are satisfactory because it aims to refer patients to nephrologists in early stages, where they may confirm the CKD diagnosis.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 183 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 183 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 31 17%
Student > Bachelor 17 9%
Student > Ph. D. Student 16 9%
Other 9 5%
Student > Postgraduate 7 4%
Other 33 18%
Unknown 70 38%
Readers by discipline Count As %
Nursing and Health Professions 29 16%
Medicine and Dentistry 17 9%
Computer Science 15 8%
Engineering 10 5%
Business, Management and Accounting 7 4%
Other 30 16%
Unknown 75 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 January 2018.
All research outputs
#14,869,034
of 23,881,329 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,124
of 2,030 outputs
Outputs of similar age
#245,394
of 447,596 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#24
of 31 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,030 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 447,596 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.